本文整理了Java中org.opencv.core.Core.bitwise_and()方法的一些代码示例，展示了Core.bitwise_and()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Core.bitwise_and()方法的具体详情如下：
包路径：org.opencv.core.Core
类名称：Core
方法名：bitwise_and

Core.bitwise_and介绍

[英]Calculates the per-element bit-wise conjunction of two arrays or an array and a scalar.

The function calculates the per-element bit-wise logical conjunction for:

• Two arrays when src1 and src2 have the same size:

dst(I) = src1(I) / src2(I) if mask(I) != 0

• An array and a scalar when src2 is constructed from Scalar or has the same number of elements as src1.channels():

dst(I) = src1(I) / src2 if mask(I) != 0

• A scalar and an array when src1 is constructed from Scalar or has the same number of elements as src2.channels():

dst(I) = src1 / src2(I) if mask(I) != 0

In case of floating-point arrays, their machine-specific bit representations (usually IEEE754-compliant) are used for the operation. In case of multi-channel arrays, each channel is processed independently. In the second and third cases above, the scalar is first converted to the array type.
[中]计算两个数组或一个数组和一个标量的每元素逐位连接。
该函数计算以下各项的每元素逐位逻辑连接：
*src1和src2具有相同大小时的两个数组：
dst（I）=src1（I）/src2（I）如果掩码（I）！=0
*当src2由Scalar构造或具有与src1.channels()相同数量的元素时，数组和标量：
dst（I）=src1（I）/src2如果掩码（I）！=0
*当src1由Scalar构造或具有与src2.channels()相同数量的元素时，标量和数组：
dst（I）=src1/src2（I）如果掩码（I）！=0
对于浮点数组，其特定于机器的位表示（通常符合IEEE754）用于操作。对于多通道阵列，每个通道都是独立处理的。在上面的第二和第三种情况下，标量首先转换为数组类型。

代码示例

代码示例来源：origin: nroduit/Weasis

public static ImageCV bitwiseAnd(Mat source, int src2Cst) {
  Objects.requireNonNull(source);
  ImageCV mask = new ImageCV(source.size(), source.type(), new Scalar(src2Cst));
  Core.bitwise_and(source, mask, mask);
  return mask;
}

代码示例来源：origin: ytai/IOIOPlotter

private void HitAndMiss(Mat src, Mat dst, Mat positive, Mat negative) {
  Imgproc.erode(src, dst, positive);
  Core.subtract(Mat.ones(src.size(), CvType.CV_8UC1), src, tmpMat_);
  Imgproc.erode(tmpMat_, tmpMat_, negative);
  Core.bitwise_and(tmpMat_, dst, dst);
}

代码示例来源：origin: kongqw/OpenCVForAndroid

public RotatedRect objectTracking(Mat mRgba) {
  rgba2Hsv(mRgba);
  updateHueImage();
  // 计算直方图的反投影。
  // Imgproc.calcBackProject(hueList, new MatOfInt(0), hist, prob, ranges, 255);
  Imgproc.calcBackProject(hueList, new MatOfInt(0), hist, prob, ranges, 1.0);
  // 计算两个数组的按位连接（dst = src1 & src2）计算两个数组或数组和标量的每个元素的逐位连接。
  Core.bitwise_and(prob, mask, prob, new Mat());
  // 追踪目标
  rotatedRect = Video.CamShift(prob, trackRect, new TermCriteria(TermCriteria.EPS, 10, 1));
  if (null != mOnCalcBackProjectListener) {
    mOnCalcBackProjectListener.onCalcBackProject(prob);
  }
  // 将本次最终到的目标作为下次追踪的对象
  trackRect = rotatedRect.boundingRect();
  Imgproc.rectangle(prob, trackRect.tl(), trackRect.br(), new Scalar(255, 255, 0, 255), 6);
  Log.i(TAG, "objectTracking: 宽度 : " + trackRect.width + "  高度 : " + trackRect.height + "  角度 : " + rotatedRect.angle);
  return rotatedRect;
}